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Stage-based assessment models are a type of fisheries stock assessment model that offer an alternative middle ground between aggregate and compositional models. We compare the capabilities of two theoretically different stage-based assessment approaches: an implementation of a biomass-based delay-difference model first described in a theoretical paper by Schnute in 1987, and an implementation of the well-known numbers-based two-stage model Catch-Survey Analysis (CSA). Models were tested within a simulation framework as well as on the real stock of white-bellied anglerfish ( Lophius piscatorius) in the Celtic Seas and Northern Bay of Biscay. For the simulated stocks, estimates from the biomass-based two-stage models were close to the true values in certain scenarios, but were sensitive to selectivity assumptions and configuration of growth within the model. CSA was more robust to selectivity assumptions, performing well in all simulated stock scenarios. Overall, results indicated that CSA was a robust stock assessment model but with relatively low precision, whereas the Schnute model was precise but required growth and mean fish weight data unaffected by selectivity.